Modern work machines often require complex control systems to control a wide range of operations. For example, the operation of an engine system of a modern work machine may be controlled by a complex engine control system. The engine control system may provide a series control functions, such as closed loop combustion control, fuel control, gear control, torque control, and/or other engine controls, etc. Physical or process models may be used to implement some functions of the engine control system. A process model may refer to a physics or mathematics based model where the state of a process is monitored by measuring process variables. A process variable may refer to a variable related to conditions of the engine under control. The engine control system may use the value of the process variable and the model to control the operation of the engine within a normal range.
Conventional process model based control systems, such as described in U.S. Pat. No. 6,823,675 to Brunell et al. on Nov. 30, 2004, often relate individual input parameters or process variables to control functions without addressing inter-correlation between individual input parameters, especially at the time of generation and/or optimization of such process models. Thus, these systems may fail to simultaneously optimize input parameter distribution requirements.
Methods and systems consistent with certain features of the disclosed systems are directed to solving one or more of the problems set forth above.